diff options
author | Mausoom Sarkar <msarkar@adobe.com> | 2015-10-13 18:35:32 +0530 |
---|---|---|
committer | Mausoom Sarkar <msarkar@adobe.com> | 2015-10-19 13:51:40 +0530 |
commit | 4c93b3dc555891ae0ad75092b6c0f77508740ecf (patch) | |
tree | c24e85107d4ff5b62fdef45a4471580a82a9669c /src/caffe | |
parent | 0151742509c23b927b0159230ac70399ac4f8967 (diff) | |
download | caffeonacl-4c93b3dc555891ae0ad75092b6c0f77508740ecf.tar.gz caffeonacl-4c93b3dc555891ae0ad75092b6c0f77508740ecf.tar.bz2 caffeonacl-4c93b3dc555891ae0ad75092b6c0f77508740ecf.zip |
Moved the loop inside PReLUParamBackward to do the reduction inside the kernel
Now PReLU backward is taking the same time as forward
Code cleanup
Removed unnecessary code
Fixed indent
merge if(channed_shared_)
Diffstat (limited to 'src/caffe')
-rw-r--r-- | src/caffe/layers/prelu_layer.cu | 44 |
1 files changed, 22 insertions, 22 deletions
diff --git a/src/caffe/layers/prelu_layer.cu b/src/caffe/layers/prelu_layer.cu index e1f20048..1225334f 100644 --- a/src/caffe/layers/prelu_layer.cu +++ b/src/caffe/layers/prelu_layer.cu @@ -31,10 +31,15 @@ __global__ void PReLUBackward(const int n, const int channels, const int dim, // CUDA kernel for element-wise parameter backward template <typename Dtype> -__global__ void PReLUParamBackward(const int n, const Dtype* in_diff, +__global__ void PReLUParamBackward(const int n, + const int rows, const int rowPitch, const Dtype* in_diff, const Dtype* in_data, Dtype* out_diff) { CUDA_KERNEL_LOOP(index, n) { out_diff[index] = in_diff[index] * in_data[index] * (in_data[index] <= 0); + for ( int k = 1; k < rows; k++ ) { + out_diff[index] += in_diff[index + k*rowPitch] + * in_data[index + k*rowPitch] * (in_data[index + k*rowPitch] <= 0); + } } } @@ -82,29 +87,24 @@ void PReLULayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, if (this->param_propagate_down_[0]) { Dtype* slope_diff = this->blobs_[0]->mutable_gpu_diff(); int cdim = channels * dim; - Dtype dsum = 0.; - for (int n = 0; n < bottom[0]->num(); ++n) { - // compute element-wise diff - // NOLINT_NEXT_LINE(whitespace/operators) - PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim), - CAFFE_CUDA_NUM_THREADS>>>( - cdim, top_diff + top[0]->offset(n), - bottom_data + bottom[0]->offset(n), - backward_buff_.mutable_gpu_diff()); - CUDA_POST_KERNEL_CHECK; - if (channel_shared_) { - Dtype d; - caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(), - multiplier_.gpu_data(), &d); - dsum += d; - } else { - caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1., - backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1., - slope_diff); - } - } + + // compute element-wise diff + // NOLINT_NEXT_LINE(whitespace/operators) + PReLUParamBackward<Dtype><<<CAFFE_GET_BLOCKS(cdim), + CAFFE_CUDA_NUM_THREADS>>>( + cdim, bottom[0]->num(), top[0]->offset(1), top_diff , + bottom_data , + backward_buff_.mutable_gpu_diff()); + CUDA_POST_KERNEL_CHECK; if (channel_shared_) { + Dtype dsum; + caffe_gpu_dot<Dtype>(channels * dim, backward_buff_.gpu_diff(), + multiplier_.gpu_data(), &dsum); caffe_gpu_add_scalar(this->blobs_[0]->count(), Dtype(dsum), slope_diff); + } else { + caffe_gpu_gemv<Dtype>(CblasNoTrans, channels, dim, 1., + backward_buff_.gpu_diff(), multiplier_.gpu_data(), 1., + slope_diff); } } // Propagate to bottom |